Model comparison
Gemini 3.8 Flash vs GPT-5.6 Sol
Google against OpenAI, compared on context, price, and attributed benchmark results.
Recorded profile verification October 1, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below
Gemini 3.8 Flash
Balanced
OpenAI
GPT-5.6 Sol
Frontier
| Specification | Gemini 3.8 Flash | GPT-5.6 Sol |
|---|---|---|
| Provider | ||
| Provider | OpenAI | |
| Tier | ||
| Tier | Balanced | Frontier |
| Context window | ||
| Context window | 1.05M | Winner: 1.05M |
| Max output | ||
| Max output | 66K | Winner: 128K |
| Input / 1M tokens | ||
| Input / 1M tokens | Winner: $0.75 | $4 |
| Output / 1M tokens | ||
| Output / 1M tokens | Winner: $3.75 | $20 |
| Weights | ||
| Weights | Closed | Closed |
| Parameters | ||
| Parameters | Not verifiedUnverified | Not verifiedUnverified |
| Reasoning levels | ||
| Reasoning levels | low, medium, high | none, low, medium, high, xhigh, max |
| Modalities | ||
| Modalities | text, image, video, audio, pdf | text, image |
| API model id | ||
| API model id | gemini-3.8-flash | gpt-5.6-sol |
| Released | ||
| Released | September 2, 2026 | July 9, 2026 |
| Artificial Analysis Intelligence Index [high] · source date Gemini 3.8 Flash: 2026-10-01 | ||
| Artificial Analysis Intelligence Index [high] · source date Gemini 3.8 Flash: 2026-10-01 | 41 | Not verifiedUnverified |
| Terminal-Bench 2.1 · source date Gemini 3.8 Flash: 2026-09-02 | ||
| Terminal-Bench 2.1 · source date Gemini 3.8 Flash: 2026-09-02 | 89.4 | Not verifiedUnverified |
| Terminal-Bench 2.1 · source date GPT-5.6 Sol: 2026-07-09 | ||
| Terminal-Bench 2.1 · source date GPT-5.6 Sol: 2026-07-09 | Not verifiedUnverified | 88.8 |
| HLE-Verified · source date Gemini 3.8 Flash: 2026-09-02 | ||
| HLE-Verified · source date Gemini 3.8 Flash: 2026-09-02 | 54.9 | Not verifiedUnverified |
| GPQA Diamond · source date GPT-5.6 Sol: 2026-07-09 | ||
| GPQA Diamond · source date GPT-5.6 Sol: 2026-07-09 | Not verifiedUnverified | 94.6 |
Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- Source date2026-10-01Artificial Analysis (Gemini 3.8 Flash (high))
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.
- Source date2026-09-02Google official blog and methodology (vendor; full 1,811-item HLE-Verified set) (Full 1,811-item verified/revised set; differs from original HLE and AA text-only HLE)
Expert reasoning on the verified and revised Humanity's Last Exam question set.
UnknownA distinct question set from original HLE and Artificial Analysis's text-only HLE. Shared dataset and publisher alone do not establish matching model effort, tools, filtering and run configuration; no quality winner is inferred without that evidence.
- Source date2026-09-02Google primary evaluation table and methodology (vendor-reported) (Vendor evaluation; Terminus-2 harness; exact run date not published)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
- Source date2026-07-09OpenAI — GPT-5.6 evals table (vendor)
Graduate-level science reasoning: multiple-choice questions written by domain PhDs.
Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.
- Source date2026-07-09OpenAI — GPT-5.6 evals table (vendor; Ultra multi-agent config: 91.9)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
Pricing tiers
Gemini 3.8 Flash: Intro Standard $0.75/$3.75 per MTok through December 31, 2026; $1.50/$7.50 from January 1, 2027. Output includes thinking tokens. Cached input $0.075, rising to $0.15; storage $0.50/MTok-hour, rising to $1. Batch/Flex input/output are $0.375/$1.875, rising to $0.75/$3.75. Priority $1.35/$6.75, rising to $2.70/$13.50. Grounding tools have separate charges and quotas; free-tier limits differ from paid.
GPT-5.6 Sol: Launch $5/$30, reduced to promotional $4/$20 on August 21; available at least through November 21, 2026. Above 272K input the full request bills $8/$30. Cached input $0.40/$0.80 short/long and cache writes $5/$10. Batch is $2/$10 at short context. Regional/FedRAMP processing adds 10% where eligible. Knowledge cutoff Feb 16 2026.
Gemini 3.8 Flash
Gemini 3.8 Flash is Google's multimodal Flash model for long-horizon software engineering and agent workflows, with current introductory API pricing through December 31, 2026.
Best for
- Agentic coding at scale
- Mid-difficulty engineering
- Multimodal pipelines
Watch out
Intro rates and cache-storage prices double January 1, 2027. Google says 3.8 can use more tokens at higher effort; 3.7 remains supported for efficiency-first workloads. Minimal thinking errors. Computer use is Preview; native audio/image generation and Live API are unsupported. Restricted Flash Cyber is a separate endpoint with no public rate confirmed.
GPT-5.6 Sol
GPT-5.6 Sol was OpenAI's GPT-5.6 flagship for complex reasoning and coding with a 1.05M-token context — superseded on 2026-09-22 by GPT-6 Sol at half the price.
Best for
- Complex reasoning
- Agentic coding
- Hard knowledge work
Watch out
GPT-6.1 Sol is the newer Sol option at $2/$10 Standard. GPT-5.6 Sol remains listed; its $4/$20 price is promotional. Max effort can increase token use, and Ultra is a multi-agent configuration rather than a separate reasoning.effort value.
Benchmark
DeepSWE 1.1 in context
Only Gemini 3.8 Flash has a published DeepSWE 1.1 result. It is shown against the wider field, with cost per completed task alongside the score.
Local leader
GPT-6 Astra [xhigh]
74.1%
Rows shown
28
Best source-matched Pass@1 per model
Snapshot date
2026-10-01
Mirrored from deepswe.datacurve.ai
Better is toward the top-right (higher pass rate, lower cost). X-axis is reversed to match DeepSWE’s public chart. v1.1 uses average cost, output tokens and steps. Legacy v1 transcriptions are excluded here until an exact primary receipt is retained.
| # | Model | Pass@1 | Cost / task | Tokens / task | Steps / task |
|---|---|---|---|---|---|
| 1 | GPT-6 Astra [xhigh] | 74.1% | $4.43 | 30k | 29 |
| 2 | Gemini 3.8 Flash [high] | 73.8% | $2.36 | 143k | 166 |
| 3 | Claude Opus 5 [max] | 73.6% | $11.84 | 118k | 99 |
| 4 | GPT-5.6 Sol [max] | 72.7% | $8.39 | 60k | 61 |
| 5 | Claude Fable 5 [xhigh] | 69.9% | $13.41 | 80k | 68 |
| 6 | GPT-5.6 Terra [max] | 69.6% | $4.95 | 72k | 76 |
| 7 | GLM 5.3 [max] | 69.0% | $3.99 | 80k | 124 |
| 8 | Kimi K3 [max] | 68.5% | $4.65 | 82k | 98 |
| 9 | Grok 4.6 [medium] | 67.5% | $3.45 | 50k | 70 |
| 10 | GPT-5.6 Luna [max] | 67.2% | $3.03 | 73k | 102 |
| 11 | GPT-5.5 [xhigh] | 67.0% | $7.23 | 46k | 82 |
| 12 | Gemini 3.7 Flash [medium] | 65.5% | $2.03 | 94k | 117 |
| 13 | GLM 5.3 Flash [max] | 63.4% | $0.48 | 73k | 123 |
| 14 | DeepSeek V4-Pro [max] | 62.8% | $0.24 | 106k | 155 |
| 15 | Claude Opus 4.8 [max] | 59.0% | $13.22 | 135k | 120 |
| 16 | Qwen3.8-Max [xhigh] | 57.5% | $3.73 | 95k | 111 |
| 17 | Muse Spark 1.2 [xhigh] | 54.9% | $3.70 | 99k | 101 |
| 18 | Claude Sonnet 5 [max] | 53.8% | $26.40 | 214k | 268 |
| 19 | Grok 4.5 [high] | 53.8% | $2.42 | 36k | 61 |
| 20 | DeepSeek V4-Flash [max] | 53.3% | $0.10 | 108k | 153 |
| 21 | Muse Spark 1.1 [xhigh] | 53.3% | $2.36 | 74k | 96 |
| 22 | GPT-5.4 [xhigh] | 51.8% | $5.65 | 71k | 70 |
| 23 | Gemini 3.6 Flash [high] | 46.7% | $4.42 | 96k | 117 |
| 24 | GLM 5.2 [max] | 43.8% | $3.92 | 78k | 129 |
| 25 | Gemini 3.5 Flash [high] | 36.1% | $3.45 | 76k | 105 |
| 26 | Kimi K2.7 Code | 30.5% | $2.82 | 59k | 149 |
| 27 | Claude Sonnet 4.6 [high] | 29.9% | $5.52 | 76k | 134 |
| 28 | Gemini 3.1 Pro [high] | 11.7% | $2.14 | 28k | 76 |
“Best per model” shows each model’s highest source-matched Pass@1 in this capture; switch to all effort levels to see every retained configuration. Efforts differ across models, so this view is not a comparison at matched effort. The source was generated September 22 and captured October 1. Small gaps may not be statistically meaningful — confirm on deepswe.datacurve.ai.
When the cheaper one wins
Gemini 3.8 Flash is cheaper on output at $3.75 per million tokens against $20 for GPT-5.6 Sol — about 5.3×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. On DeepSWE 1.1, Gemini 3.8 Flash is 73.8% Pass@1 at $2.36/task versus GPT-5.6 Sol at 72.7% / $8.39/task. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.
Run the model pickerEvidence confidence: Medium
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: parameter count.
- No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
- Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- Gemini 3.8 Flash: Google primary profile — whole-record source check October 1 (accessed 2026-10-01)
- Gemini 3.8 Flash: Gemini current API pricing and cache-storage tiers (accessed 2026-10-01)
- Gemini 3.8 Flash: Google — Gemini 3.8 Flash and 3.8 Flash Cyber (accessed 2026-09-03)
- Gemini 3.8 Flash: Gemini API pricing (gemini-3.8-flash $0.75/$3.75 intro) (accessed 2026-09-03)
- Gemini 3.8 Flash: Datacurve DeepSWE 1.1 — 3.8 Flash high 73.8%, $2.36/task; Google separately self-reports 73.7% (accessed 2026-09-03)
- GPT-5.6 Sol: OpenAI model card — whole-profile source check October 1 (accessed 2026-10-01)
- GPT-5.6 Sol: OpenAI current API pricing and processing tiers (accessed 2026-10-01)
- GPT-5.6 Sol: OpenAI — GPT-5.6 (accessed 2026-08-29)
- GPT-5.6 Sol: OpenAI API pricing (gpt-5.6-sol $4/$20) (accessed 2026-08-29)
- GPT-5.6 Sol: OpenAI — Introducing GPT-6 Sol and Luna (GPT-5.6 Sol successor) (accessed 2026-09-26)
Related comparisons
- Amazon Nova 2 Lite vs Gemini 3.8 Flash
- Amazon Nova 2 Pro vs Gemini 3.8 Flash
- Base 1 vs Gemini 3.8 Flash
- Claude Fable 5.1 vs GPT-5.6 Sol
- Claude Fable 5 vs GPT-5.6 Sol
- Claude Mythos 5.1 vs GPT-5.6 Sol
Diving deeper on one model? Gemini 3.8 Flash · GPT-5.6 Sol
Common questions
Gemini 3.8 Flash vs GPT-5.6 Sol
Answered from the verified figures on this page rather than general guidance.
Is Gemini 3.8 Flash or GPT-5.6 Sol cheaper for input?
Is Gemini 3.8 Flash or GPT-5.6 Sol cheaper for output?
Which has the larger context window, Gemini 3.8 Flash or GPT-5.6 Sol?
Do Gemini 3.8 Flash and GPT-5.6 Sol support the same reasoning levels?
Should I use Gemini 3.8 Flash or GPT-5.6 Sol?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: Gemini 3.8 Flash costs $0.75 per 1M tokens versus $4 for GPT-5.6 Sol — a 5.3x difference at the headline tier.
- Context: GPT-5.6 Sol takes 1.05M against 1.05M for Gemini 3.8 Flash — only decisive if your prompts approach the smaller window.
- Positioning: Gemini 3.8 Flash sits in the balanced tier, GPT-5.6 Sol in the frontier tier. Compare accuracy, reliability, latency, and cost on representative tasks before choosing.
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.